"""
Aggregate all per-model JSON results and print a comparison table.

Usage:
  python compare_results.py \
      --results-dir /mnt/bn/bohanzhainas1/jiashuo/exp/active_cases_eval

Output: a markdown table + CSV written to results_dir/comparison_table.csv
"""

import argparse
import csv
import json
from pathlib import Path


def load_result(path: Path) -> dict | None:
    try:
        d = json.loads(path.read_text())
        return d
    except Exception as e:
        print(f"  Warning: could not load {path.name}: {e}")
        return None


def format_pct(v: float | None) -> str:
    if v is None:
        return "N/A"
    return f"{v*100:.1f}%"


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--results-dir",
                        default="/mnt/bn/bohanzhainas1/jiashuo/exp/active_cases_eval")
    parser.add_argument("--output-csv", default=None,
                        help="Path for output CSV (default: results_dir/comparison_table.csv)")
    args = parser.parse_args()

    results_dir = Path(args.results_dir)
    out_csv = args.output_csv or str(results_dir / "comparison_table.csv")

    # Also save outputs to the inference root dir
    inference_dir = Path("/mlx/users/jiashuo.fan/playground/inference")
    inference_dir.mkdir(parents=True, exist_ok=True)

    result_files = sorted(results_dir.glob("*.json"))
    if not result_files:
        print(f"No result files found in {results_dir}")
        return

    rows = []
    for f in result_files:
        if f.name == "comparison_table.json":
            continue
        d = load_result(f)
        if d is None:
            continue

        model_name = d.get("model_name", f.stem)
        accuracy   = d.get("accuracy", 0.0)
        correct    = d.get("correct", 0)
        evaluated  = d.get("evaluated", 0)
        parse_fail = d.get("parse_failures", 0)
        per_class  = d.get("per_class", {})

        p1 = per_class.get("1", {})
        p0 = per_class.get("0", {})

        # For the 238 hard cases:
        # - gt is ALWAYS 1 (all are true positives that model missed)
        # - recall@1 = fraction that the VLM correctly identifies as label=1
        recall_1   = p1.get("recall",    None)
        precision_1= p1.get("precision", None)
        f1_1       = p1.get("f1",        None)
        recall_0   = p0.get("recall",    None)

        rows.append({
            "model":        model_name,
            "accuracy":     accuracy,
            "correct":      correct,
            "evaluated":    evaluated,
            "parse_fail":   parse_fail,
            "recall_1":     recall_1,
            "precision_1":  precision_1,
            "f1_1":         f1_1,
            "recall_0":     recall_0,
        })

    if not rows:
        print("No results to display.")
        return

    # Sort by accuracy descending
    rows.sort(key=lambda r: r["accuracy"], reverse=True)

    # Print markdown table
    header = (
        f"{'Model':<30} | {'Acc':>6} | {'Correct':>8} | "
        f"{'Rec@1':>7} | {'Prec@1':>7} | {'F1@1':>6} | "
        f"{'Evaluated':>9} | {'ParseFail':>9}"
    )
    sep = "-" * len(header)
    print("\n" + sep)
    print(header)
    print(sep)
    for r in rows:
        print(
            f"{r['model']:<30} | {format_pct(r['accuracy']):>6} | "
            f"{r['correct']:>5}/{r['evaluated']:<3} | "
            f"{format_pct(r['recall_1']):>7} | "
            f"{format_pct(r['precision_1']):>7} | "
            f"{format_pct(r['f1_1']):>6} | "
            f"{r['evaluated']:>9} | "
            f"{r['parse_fail']:>9}"
        )
    print(sep)
    print(f"\nNote: All 238 cases have gt_label=1 (hard false-negatives of the existing model).")
    print(f"      Acc / Recall@1 = fraction correctly identified as causally related.")

    # Save markdown table
    md_lines = [
        "# VLM Comparison on 238 Hard Cases (False Negatives)\n",
        "> All 238 cases have gt_label=1. Acc / Recall@1 = fraction correctly identified as causally related.\n",
        "",
        f"| {'Model':<30} | {'Acc':>6} | {'Correct':>8} | {'Rec@1':>7} | {'Prec@1':>7} | {'F1@1':>6} | {'Evaluated':>9} | {'ParseFail':>9} |",
        f"|{'-'*32}|{'-'*8}|{'-'*10}|{'-'*9}|{'-'*9}|{'-'*8}|{'-'*11}|{'-'*11}|",
    ]
    for r in rows:
        md_lines.append(
            f"| {r['model']:<30} | {format_pct(r['accuracy']):>6} | "
            f"{r['correct']:>5}/{r['evaluated']:<3} | "
            f"{format_pct(r['recall_1']):>7} | "
            f"{format_pct(r['precision_1']):>7} | "
            f"{format_pct(r['f1_1']):>6} | "
            f"{r['evaluated']:>9} | "
            f"{r['parse_fail']:>9} |"
        )
    md_text = "\n".join(md_lines)
    print(md_text)

    # Save to both results_dir and inference root
    for out_dir in [results_dir, inference_dir]:
        md_path = out_dir / "model_comparison.md"
        md_path.write_text(md_text)
        print(f"Markdown saved to: {md_path}")

    # Save CSV
    fieldnames = ["model", "accuracy", "correct", "evaluated", "parse_fail",
                  "recall_1", "precision_1", "f1_1", "recall_0"]
    for out_dir in [results_dir, inference_dir]:
        csv_path = out_dir / "model_comparison.csv"
        with open(csv_path, "w", newline="") as f:
            writer = csv.DictWriter(f, fieldnames=fieldnames)
            writer.writeheader()
            writer.writerows(rows)
        print(f"CSV saved to: {csv_path}")

    # Save comparison JSON for further analysis
    comp_json = results_dir / "comparison_table.json"
    comp_json.write_text(json.dumps(rows, indent=2))
    print(f"JSON saved to:  {comp_json}")


if __name__ == "__main__":
    main()